Printed circuit assembly manufacturing
Printed circuit assembly manufacturing
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Support vector machines for dynamic reconstruction of a chaotic system
Advances in kernel methods
Using support vector machines for time series prediction
Advances in kernel methods
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A tutorial on support vector regression
Statistics and Computing
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Review: A review of data mining applications for quality improvement in manufacturing industry
Expert Systems with Applications: An International Journal
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A printed circuit board (PCB) is an essential part of modern electronic circuits. It is made of a flat panel of insulating materials with patterned copper foils that act as electric pathways for various components such as ICs, diodes, capacitors, resistors, and coils. The size of PCBs has been shrinking over the years, while the number of components mounted on these boards has increased considerably. This trend makes the design and fabrication of PCBs ever more difficult. At the beginning of design cycles, it is important to estimate the time to complete the steps required accurately, based on many factors such as the required parts, approximate board size and shape, and a rough sketch of schematics. Current approach uses multiple linear regression (MLR) technique for time and cost estimations. However, the need for accurate predictive models continues to grow as the technology becomes more advanced. In this paper, we analyze a large volume of historical PCB design data, extract some important variables, and develop predictive models based on the extracted variables using a data mining approach. The data mining approach uses an adaptive support vector regression (ASVR) technique; the benchmark model used is the MLR technique currently being used in the industry. The strengths of SVR for this data include its ability to represent data in high-dimensional space through kernel functions. The computational results show that a data mining approach is a better prediction technique for this data. Our approach reduces computation time and enhances the practical applications of the SVR technique.